An estimated 187,600 new cases of cancer are expected in Canada in 2013 [1] with radiation therapy (RT) indicated as part of the patient's management in approximately 40 percent of cancer cases [2]. The delivery of RT for the treatment of cancer typically is a complicated process that requires both clinical and technical expertise in order to generate treatment plans that are safe and effective for the treatment of cancer.
For the RT process, patients are imaged with computed tomography (CT) imaging and optionally with multi-modality imaging (e.g. MR, PET) depending on the treatment site. Regions of interest (ROIs) i.e. targets (the locations radiation is directed to) and normal tissue structures (the locations radiation is minimized to) are delineated manually and/or semi-automatically on the acquired images (a). Treatment plans are generated manually, in which the direction of radiation beams and the clinical objectives of the treatment must be specified. An optimization algorithm is then used to generate the intensity and/or shape and/or modulation of radiation beams to achieve the treatment objectives (b). A dose distribution, a spatial representation of the radiation dose the patient will receive, can then be calculated. Therefore, the dose distribution (also referred to as a dose map) is directly connected with the anatomical imaging acquired from the RT process to relate the dose and spatial information specific to the patient.
In addition, the dose distribution is used to quantitatively evaluate the dose received by the delineated ROIs for assessing treatment plan quality and safety (c). The steps (a-c) are repeated until an acceptable plan is generated. Finally, the completed treatment plans are then reviewed by the multi-disciplinary RT team for quality, safety and compliance with established clinical protocols before the treatment plan will be delivered to the patient.
RT Quality Assurance
The RT treatment plan quality assurance (QA) process typically relies on the vigilance of the multi-disciplinary team to review and assimilate relatively complex data from different sources. Human vigilance has been found to be effective in the treatment plan QA process in about 80 percent of cases [3] and for preventing treatment incidents in about 98 percent of cases [4,5]. As a result, sub-optimal treatment plans, which have the potential to result in a significant detriment to the patient, may be used clinically. Several studies have shown treatment plans, which deviate from established QA guidelines, result in worse patient outcomes [6,7]. Therefore, the current RT process may require substantial multi-disciplinary QA resources to reduce the likelihood of errors and to ensure a high standard of patient care.
The multi-disciplinary RT team comprising radiation therapists, physicists and oncologists typically reviews each proposed treatment plan for clinical and technical merit. This review typically includes assessing safety (e.g., that the proposed plan does not exceed any normal tissue dose tolerances), deliverability (e.g., the dose calculated in the proposed treatment plan can be reproduced on the treatment unit), consistency in the transfer of data between databases (e.g., the parameters defining the proposed plan are the same parameters to actually treat the specific patient) and overall quality (e.g., the proposed plan is consistent with other plans for the given site and technique in terms of the dose prescription, the dose distribution, target coverage etc.) [8-19].
This process is typically largely manual and complex, as there may be numerous parameters that require human expert review. This has lead to an interest in automated QA methods in order to reduce the reliance on human vigilance [20-23]. Methods developed to date have shown promise only in a limited clinical scope.
RT Planning
Technical innovations in RT have improved the quality of treatment plans usually at the cost of increased complexity. However, treatment planning still remains a highly manual process, which requires users to delineate numerous regions of interest (ROIs) for treatment planning and set treatment objectives for an optimization engine to solve. For example, optimization objectives may specify the target ROI must receive >95% of the prescription dose to >95% of the target volume while a healthy organ must receive <100% of the prescription dose to 1 cc of the organ volume.
The process almost always involves multiple iterations, as changes to the objectives and the ROIs themselves are required to generate an acceptable treatment plan. To date, conventional automated treatment planning methods have focused on setting objectives and then optimizing those objectives to generate the dose distribution (also referred to as a dose map). Such a process still requires ROI delineation, beam placement, and manual adjustment of the objectives.
In addition, the variation in ROI delineation and treatment plan quality is well-established [24, 25]. The use of automation may help to improve consistency and add standardization to the process [26].